Job Application Research & Tailoring Agent

An AI-powered multi-agent pipeline built with LangGraph, LangChain, and Google Gemini to research companies and tailor CVs for specific job descriptions.

Features

• JD Ingestion: Supports URLs (web scraping) and local PDF/TXT files.
• JD Validation: Automatically verifies if the source is a valid job description before starting research.
• Parallel Intelligence: Simultaneously researches the Hiring Company, Role Best Practices, and LinkedIn Competing Candidate Profiles.
• Recursive Refinement: Evaluates research for gaps and loops back to refine data (up to 3 iterations).
• ATS-Optimized CV: Produces a professionally styled PDF and Markdown CV using structured YAML as the "Source of Truth".
• Organized Outputs: Each run is saved in a unique, timestamped directory.

Workflow Architecture

The agent uses a parallel fan-out/fan-in pattern with a self-correcting refinement loop:

Setup

1. Install Dependencies:

   pip install -r requirements.txt

2. Configure Environment: Create a .env file in the root directory:

   GOOGLE_API_KEY=your_gemini_api_key
   TAVILY_API_KEY=your_tavily_api_key
   USER_AGENT="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)..." 

Usage

Run with Defaults

Uses the built-in sample CV and a default Apple MLE job description:

python main.py

Tailor for a Specific Job

python main.py --cv data/base_cv/master_cv.yaml --jd "https://company.com/job-url"

Command Line Options

Argument	Description	Default
--cv	Path to your original CV (YAML, PDF, or TXT)	data/base_cv/master_cv.yaml
--jd	URL or path to the Job Description	(Apple MLE Job URL)
--model	Gemini model to use	gemini-2.5-flash
--personalize	Custom instructions for the LLM	None
--visualize	Generate and save the workflow diagram	False
--pdf	Render an existing Markdown file to styled PDF	None

Outputs

Each execution creates a folder in data/output/YYYYMMDD_HHMMSS/ containing:

• TAILORED_CV.pdf: The final professionally formatted resume.
• TAILORED_CV.md: Markdown version of the tailored resume.
• STRATEGY_REPORT.pdf: Detailed research and tailoring strategy.
• STRATEGY_REPORT.md: Markdown version of the research report.
• metadata.json: Logs the inputs and model used for that specific run.